AlexNet网络结构与pytorch代码实现

一  网络结构

 

 

二  pytoch代码实现

　　方法一

import time
import torch
from torch import nn, optim
import torchvision
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 96, 11, 4), # in_channels, out_channels, kernel_size, stride, padding
            nn.ReLU(),
            nn.MaxPool2d(3, 2), # kernel_size, stride
            # 减小卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数
            nn.Conv2d(96, 256, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(3, 2),
            # 连续3个卷积层，且使用更小的卷积窗口。除了最后的卷积层外，进一步增大了输出通道数。
            # 前两个卷积层后不使用池化层来减小输入的高和宽
            nn.Conv2d(256, 384, 3, 1, 1),
            nn.ReLU(),
            nn.Conv2d(384, 384, 3, 1, 1),
            nn.ReLU(),
            nn.Conv2d(384, 256, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(3, 2)
        )
         # 这里全连接层的输出个数比LeNet中的大数倍。使用丢弃层来缓解过拟合
        self.fc = nn.Sequential(
            nn.Linear(256*5*5, 4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            # 输出层。由于这里使用Fashion-MNIST，所以用类别数为10，而非论文中的1000
            nn.Linear(4096, 10),
        )

    def forward(self, img):
        feature = self.conv(img)
        output = self.fc(feature.view(img.shape[0], -1))
        return output

　　方法二

import torch
import torch.nn as nn


class Conv2dReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=(1, 1), padding=(0, 0), bias=True):
        super(Conv2dReLU, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.bias = bias
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        """
        Args:
            x: [N,C,H,W]
        """
        o1 = self.conv(x)
        o2 = self.relu(o1)
        return o2


class Features(nn.Module):
    def __init__(self):
        super(Features, self).__init__()

        self.conv2drelu1 = Conv2dReLU(in_channels=3, out_channels=64, kernel_size=11, stride=4, padding=2)
        # [1,64,55,55]
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        # [1,64,27,27]
        self.conv2drelu2 = Conv2dReLU(in_channels=64, out_channels=192, kernel_size=5, padding=2)
        # [1,192,27,27]
        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        # [1,192,13,13]
        self.conv2drelu3 = Conv2dReLU(in_channels=192, out_channels=384, kernel_size=3, padding=1)
        # [1,384,13,13]
        self.conv2drelu4 = Conv2dReLU(in_channels=384, out_channels=256, kernel_size=3, padding=1)
        # [1,256,13,13]
        self.conv2drelu5 = Conv2dReLU(in_channels=256, out_channels=256, kernel_size=3, padding=1)
        # [1,256,13,13]
        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2)
        # [1,256,6,6]

    def forward(self, x):
        """
        Args:
            x: [N,C,H,W]
        """
        o1 = self.conv2drelu1(x)
        o2 = self.maxpool1(o1)
        o3 = self.conv2drelu2(o2)
        o4 = self.maxpool2(o3)
        o5 = self.conv2drelu3(o4)
        o6 = self.conv2drelu4(o5)
        o7 = self.conv2drelu5(o6)
        o8 = self.maxpool3(o7)
        return o8


class Classifier(nn.Module):
    def __init__(self, num_classes):
        super(Classifier, self).__init__()

        self.num_classes = num_classes

        self.dropout1 = nn.Dropout()
        self.fc1 = nn.Linear(in_features=9216, out_features=4096)
        self.relu1 = nn.ReLU(inplace=True)
        self.dropout2 = nn.Dropout()
        self.fc2 = nn.Linear(in_features=4096, out_features=4096)
        self.relu2 = nn.ReLU(inplace=True)
        self.fc3 = nn.Linear(in_features=4096, out_features=num_classes)

    def forward(self, x):
        o1 = self.dropout1(x)
        o2 = self.fc1(o1)
        o3 = self.relu1(o2)
        o4 = self.dropout2(o3)
        o5 = self.fc2(o4)
        o6 = self.relu2(o5)
        o7 = self.fc3(o6)
        return o7


class AlexNet(nn.Module):
    def __init__(self, num_classes):
        super(AlexNet, self).__init__()

        self.num_classes = num_classes

        self.features = Features()
        self.aavgpool = nn.AdaptiveAvgPool2d(output_size=6)
        self.flatten = nn.Flatten()
        self.classifier = Classifier(num_classes=num_classes)

    def forward(self, x):

        o1 = self.features(x)
        o2 = self.aavgpool(o1)
        o3 = self.flatten(o2)
        o4 = self.classifier(o3)
        return o4

if __name__ == '__main__':
    model = AlexNet(4)
    for name, parameters in model.named_parameters():
        print(name, ':', parameters.size())
    data = torch.rand(1,3,224,224)
    a = model(data)
    print(a)